Data and Analytics Engineer

At SANDISK, our vision is to power global innovation and push the boundaries of technology to make what you thought was once impossible, possible.

At our core, SANDISK is a company of problem solvers. People achieve extraordinary things given the right technology. For decades, we’ve been doing just that. Our technology helped people put a man on the moon.

We are a key partner to some of the largest and highest growth organizations in the world. From energizing the most competitive gaming platforms, to enabling systems to make cities safer and cars smarter and more connected, to powering the data centers behind many of the world’s biggest companies and public cloud, SANDISK is fueling a brighter, smarter future.

Binge-watch any shows, use social media or shop online lately? You’ll find SANDISK supporting the storage infrastructure behind many of these platforms. And, that flash memory card that captures and preserves your most precious moments? That’s us, too.

We offer an expansive portfolio of technologies, storage devices and platforms for business and consumers alike.

Today’s exceptional challenges require your unique skills. It’s You & SANDISK. Together, we’re the next BIG thing in data

We are seeking a hands-on Technical Data & Analytics Engineer to design, build, and operationalize analytics-ready data products, semantic models, and self-service BI solutions that enable data-driven decision-making across the enterprise. The ideal candidate bridges the gap between data engineering and business analytics — transforming raw data into curated, trusted, and performant datasets that power dashboards, KPIs, and advanced analytics use cases. This role partners closely with business stakeholders, data engineers, BI developers, and functional leaders across procurement, supply chain, finance, and operations.

Responsibilities

Design and build curated, analytics-ready data models (star/snowflake schemas, medallion architecture — bronze/silver/gold layers) on modern platforms like Databricks,; Develop and maintain transformation pipelines using dbt (data build tool) , SQL, and PySpark to convert raw enterprise data into trusted, business-friendly datasets.; Partner with business teams to translate KPIs (e.g., spend analytics, supplier performance, demand forecast accuracy, working capital) into reusable, self-service datasets .; Build, optimize, and govern enterprise dashboards and reports in Power BI, Tableau etc; Design and maintain ELT pipelines to ingest data from SAP S/4HANA, Oracle, Salesforce, Workday, and third-party APIs into the enterprise data lake/warehouse.; Champion data lineage, cataloging, and governance using tools like Collibra, Purview, Alation, or Unity Catalog; Implement data quality checks, tests, and observability using dbt tests, Great Expectations, Monte Carlo

Requirements

4–6 years of professional experience in analytics engineering, data engineering, or BI development roles.; Proven track record of delivering at least 2–3 production analytics data products end-to-end.; Bachelor's or Master's in Computer Science, Data Science, Information Systems, Engineering, Statistics, or a related field.; Experience with enterprise ERP data (SAP S/4HANA, Oracle) and modeling for procurement, supply chain, or finance domains.; Understanding of AI/ML fundamentals and how analytics datasets feed ML pipelines.; Exposure to spend analytics, supplier scorecards, demand planning, or working capital analytics .